System and method for feature extraction from real-time vehicle kinetics data for remote tire wear modeling

ABSTRACT

A system and method are provided for efficiently estimating vehicle tire wear. Vehicle kinetics (first) data are provided via one or more sensors associated with the vehicle and/or at least one associated tire. The vehicle kinetics data are locally processed to compress or otherwise generate second data as a reduced subset thereof, said second data representative of the first data and comprising any one or more predetermined wear-specific features extracted therefrom. The second data are selectively transmitted via a communications network to a remote computing system, which processes the second data to estimate a wear characteristic for the at least one tire. Alternatively, the second data processed to generate third data as a reconstruction of the first data, and the third data and the any one or more extracted features are processed to estimate a wear characteristic for the at least one tire.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application is a continuation of International Application NumberPCT/US2020/026658, filed Mar. 20, 2020, and further claims benefit ofU.S. Provisional Patent Application Nos. 62/827,339, filed Apr. 1, 2019,62/843,863, filed May 6, 2019, 62/883,252, filed Aug. 6, 2019,62/889,684, filed Aug. 21, 2019, and 62/911,496, filed Oct. 7, 2019,each of which is hereby incorporated by reference in its entirety.

FIELD OF THE INVENTION

The present invention relates generally to the modeling and predictingof tire performance and the provision of feedback based thereon. Moreparticularly, an embodiment of an invention as disclosed herein relatesto system and method for summarizing high frequency vehicle data inorder to transmit extracted relevant features for remotely estimatingvehicle tire wear.

BACKGROUND

Prediction of tire wear and corresponding tire traction capabilities isan important tool for anyone owning or operating vehicles, particularlyin the context of vehicle fleet management. As tires are used, it isnormal for the tread to gradually become shallower and overall tireperformance to change. At a certain point it becomes critical to beaware of the tire conditions, as insufficient tire tread can createunsafe driving conditions. For example, when road conditions arenon-optimal the tires may be unable to grip the road and a driver maylose control of his or her vehicle. Generally stated, the shallower thetire tread, the more easily the driver may lose traction, particularlywhen driving at improper speeds or in adverse weather conditions such asrain, snow, or the like.

In addition, irregular tread wear may occur for a variety of reasonsthat may lead users to replace a tire sooner than would otherwise havebeen necessary. Vehicles, drivers, and individual tires are alldifferent from each other, and can cause tires to wear at very differentrates. For instance, high performance tires for sports cars wear morequickly than touring tires for a family sedan. However, a wide varietyof factors can cause a tire to wear out sooner than expected, and/orcause it to wear irregularly and create noise or vibration. Two commoncauses of premature and/or irregular tire wear are improper inflationpressure and out-of-spec alignment conditions.

Tire wear is known to progress in a non-linear fashion throughout thelife of a tire. One primary reason for this is that as the tread wearsover time, the tread blocks become stiffer. In addition, the treadpatterns are typically designed to have less void area as the tirewears. Either or both of these characteristics can contribute to aslower wear rate.

The focus of most tire wear predictions is on the initial wear rate—thewear rate when the tire is brand new. This is at least in part becausethe tire industry is typically concerned with new tire performance ingeneral, due to having to meet original equipment manufacturer (OEM)requirements. To predict the performance of a tire for the entirety ofits life, a new wear model is required.

However, tire wear is a complex phenomenon to model. There are accuratemodels currently in existence that utilize finite element analysis(FEA), but these simulations can typically take weeks to complete. If itis desired to simulate the wear rate at several different tread depths,this would further take months of computationally expensive simulations.

It would be desirable to provide users with substantially real-timepredictions about the performance and capabilities of their tires. Suchinformation may lead not only to greater customer satisfaction, but toless accidents generally. A large percentage of tire-related accidentsoccur due to low tread depth and the corresponding lack of traction, andit would be desirable to provide the end-user with appropriaterecommendations regarding the same.

It would further be desirable to estimate the traction capabilities ofthe tire, and provide such feedback as an input to models for otheruseful/actionable predictions or control loops.

It would further be desirable to estimate the tread depth of the tire,and provide such feedback as an input to models for otheruseful/actionable predictions such as for example traction, fuelefficiency, durability, etc. An accurate tread depth prediction is thefirst step to predicting numerous other tire performance areas.

It would further be desirable to provide these services as part of adistributed and relatively automated tire-as-a-service model, withoutrequiring manual tread depth measurements such as for example wouldtypically be provided by field engineers and/or with dedicatedequipment.

It is known to produce high frequency vehicle data and/or tire data forthe purpose of determining vehicle conditions at a given time. However,continuous collection of streaming data results in an overwhelmingvolume of data points, which is typically impractical from each of adata transmission, storage, and processing perspective. It would furtherbe desirable to improve the state of knowledge based on measurements oftread depth, providing real time feedback to users (e.g., individualdrivers, fleet managers, other equivalent end-users) based on animproved ability to predict the wear life left in a tire based on a fewperiodic measurements, and thereby enabling the user to achieve maximumvalue out of the tire.

BRIEF SUMMARY

In a first exemplary embodiment as disclosed herein, the aforementionedobjects may be attained via a computer-implemented method for modelingand predicting of tire performance and the provision of feedback basedthereon. The method includes collecting vehicle data for a vehicleand/or tire data for at least one tire associated with the vehicle, anddetermining a current tire wear status in real-time for the at least onetire, based at least in part on the collected data. One or more tireperformance characteristics are predicted, based at least in part on thedetermined tire wear status and the collected data. Real-time feedbackis selectively provided, based on the predicted one or more tireperformance characteristics and/or determined current tire wear status.

Additional advantageous features are further realized in an exemplaryvariant on the above-referenced first embodiment, wherein a secondembodiment of the computer-implemented method is disclosed herein forestimating a tire wear status, and comprises accumulating in datastorage information regarding probability distributions corresponding toeach of a respective plurality of tire wear factors. Vehicle data and/ortire data comprising movement data and location data collected inassociation with a vehicle is transmitted from the vehicle to a remoteserver. At least one observation corresponding to one or more of theplurality of factors is generated based on the transmitted vehicle data.A Bayesian estimation of a tire wear status at a given time is generatedfor at least one tire associated with the vehicle, based at least on theat least one generated observation and the stored information regardingprobability distributions.

One exemplary aspect of the aforementioned second embodiment may includestoring information regarding updated probability distributionscorresponding to a respective plurality of factors contributing to tirewear for the at least one tire associated with the vehicle, based atleast on the generated at least one observation.

Another exemplary aspect of the aforementioned second embodiment mayinclude predicting a tire wear status at one or more future parametersfor the at least one tire associated with the vehicle. For example, thetire wear status may be predicted with respect to an upcoming period oftime that the vehicle is driven, or with respect to an upcoming distanceto be traveled.

Another exemplary aspect of the aforementioned second embodiment mayinclude predicting a replacement time for the at least one tireassociated with the vehicle, based on a current tire wear status or thepredicted tire wear status as compared with tire wear thresholdsassociated with the at least one tire associated with the vehicle.

In another exemplary aspect of the aforementioned second embodiment, theinformation regarding the plurality of probability distributions mayreflect an array of time-series characterization curves.

Another exemplary aspect of the aforementioned second embodiment mayinclude receiving one or more tire wear input values from a user via auser interface associated with the remote server and generating at leastone observation for one or more of the plurality of factors based on theone or more tire wear input values.

Another exemplary aspect of the aforementioned second embodiment mayinclude receiving one or more tire wear input values generated by one ormore sensors mounted in or on a respective tire of the at least one tireand generating at least one observation for one or more of the pluralityof factors based on the one or more tire wear input values.

Another exemplary aspect of the aforementioned second embodiment mayinclude receiving one or more tire wear input values generated by asensor external to the vehicle and generating at least one observationfor one or more of the plurality of factors based on the one or moretire wear input values.

In another exemplary aspect of the aforementioned second embodiment, atleast one of the tire wear input values generated by the sensor externalto the vehicle comprises a tread depth measurement.

Another exemplary aspect of the aforementioned second embodiment mayinclude generating an estimated tire wear status with a baseline valueand a range corresponding to a confidence level for the estimation.

A system may be provided in accordance with the above-referenced secondembodiment to estimate a tire wear status. The system may include a datastorage network having stored thereon information regarding probabilitydistributions corresponding to each of a respective plurality of tirewear factors. For each of a plurality of vehicles, distributed computingnodes are linked to one or more vehicle-mounted sensors respectivelyconfigured to collect vehicle data. A server-based computing network isprovided comprising computer readable media having instructions residingthereon and executable by one or more processors to direct theperformance of aspects previously recited with respect to the secondembodiment.

Additional advantageous features are further realized in anotherexemplary variant on the above-referenced first embodiment, wherein athird embodiment of the computer-implemented method is disclosed hereinfor an analytical tire wear model utilizing a brush-type model. Thebrush-type model is a simplified tire model that models the treadelements as independent “brush bristles” which greatly reduces thecomplexity of modeling the contact interface between the road andrubber. This model can capture the first order effects (tread blockstiffening and contact area increasing) that occurs in a real tire as itwears.

In accordance with the third exemplary embodiment, an original treaddepth is determined for a tire associated with a vehicle, and an initialwear rate is determined for the tire based at least partially on theoriginal tread depth. One or more tire conditions are measured astime-series inputs to a predictive tire wear model. A current wear rateis normalized based on said inputs with respect to the initial wear ratefor the tire, wherein a tire wear status of the tire can be predictedfor one or more specified future parameters.

In one aspect of the aforementioned third embodiment, the current wearrate is further determined at least in part based on a brush-type tirewear model for a contact interface between a base material of the tireand a road surface, wherein the interface is represented as a pluralityof independently deformable elements.

In another aspect of the aforementioned third embodiment, the measuredone or more tire conditions comprise detected contact areas and voidareas corresponding to tire tread depths.

In another aspect of the aforementioned third embodiment, the one ormore specified future parameters are associated with a time traveled.

Alternatively, the one or more specified future parameters may beassociated with a distance traveled.

In another aspect of the aforementioned third embodiment, a replacementtime for the tire may be predicted based on the predicted tire wearstatus as compared with one or more predetermined tire wear thresholdsassociated with the tire.

In another aspect of the aforementioned third embodiment, an alert isgenerated to a user associated with the vehicle based on the predictedreplacement time.

In another aspect of the aforementioned third embodiment, one or moremeasured conditions are received from a user via a user interface.

In another aspect of the aforementioned third embodiment, one or more ofthe measured conditions are generated by one or more sensors mounted inor on the tire and received therefrom.

In another aspect of the aforementioned third embodiment, one or moremeasured conditions are generated by a sensor external to the vehicleand received therefrom. At least one of the tire wear input valuesgenerated by the sensor external to the vehicle may comprise a treaddepth measurement.

In another aspect of the aforementioned third embodiment, a tirerotation threshold event and/or an alignment threshold event may bepredicted by the system based at least partially on the time-seriesinputs and/or the predicted tire wear status. An alert may accordinglybe generated to a user interface associated with the vehicle basedthereon. The user interface may be a static display mounted in thevehicle, a display for a mobile computing device associated with adriver of the vehicle, etc.

In another aspect of the aforementioned third embodiment, an optimaltype of tire for the vehicle may be predicted based at least in part onthe time-series inputs and/or the predicted tire wear status. An alertmay accordingly be generated to a user interface associated with thevehicle based thereon. The user interface may be a static displaymounted in the vehicle, a display for a mobile computing deviceassociated with a driver of the vehicle, etc.

In an embodiment, a system may be provided for predicting progression invehicle tire wear in accordance with the aforementioned thirdembodiment, comprising a server functionally linked to a data storagenetwork. The data storage network includes an original tread depth for atire associated with a vehicle, and a predictive tire wear model. One ormore sensors are provided and configured to provide signalscorresponding to measured tire conditions. The server is configured todetermine an initial wear rate for the tire based on the original treaddepth and the tire wear model, collect the signals corresponding to themeasured tire conditions as time-series inputs to the predictive tirewear model, normalize a current wear rate based on said inputs to theinitial wear rate for the tire, and predict a tire wear status of thetire for one or more specified future parameters.

In one exemplary aspect of the system according to the third embodiment,the wear rate may be modeled using a brush-type tire wear model for acontact interface between a base material of the tire and a roadsurface, wherein the interface is represented as a plurality ofindependently deformable elements. Alternative physics-based tire wearmodels may also be implemented within the scope of the presentdisclosure, including but not limited to FEA models.

Additional advantageous features are further realized in an exemplaryvariant on the above-referenced first embodiment, wherein a fourthembodiment of the computer-implemented method is disclosed herein forestimating progression in vehicle tire wear. The method according to thefourth embodiment includes storing a tread depth at a first (e.g.,initial or unworn) stage for a tire associated with a vehicle. Themethod further includes sensing and storing a first set of one or moremodal frequencies for the tire at the first stage, responsive to animpact associated with a first modal analysis. At a subsequent second(e.g., at least partially worn) stage, a second set of a correspondingone or more modal frequencies for the tire are sensed, responsive to animpact associated with a second modal analysis. Based on a calculatedfrequency shift between at least one corresponding modal frequency fromeach of the first and second sets, a tire wear status of the tire may beestimated at the second stage.

In one exemplary aspect of the aforementioned fourth embodiment, a massof the tire is stored at the first stage, wherein the step of estimatingthe tire wear status at the second stage comprises determining a changein mass of the tire between the first and second stages based on thecalculated frequency shift.

In another exemplary aspect of the aforementioned fourth embodiment, anestimated loss in tire tread is determined in relation to the change inmass of the tire between the first and second stages based on thecalculated frequency shift. Alternatively, an estimated loss in tiretread may be determined via a retrievable correlation between anobserved frequency shift and a change in tire tread for a given tire.The correlation may for example be retrieved from data storage withrespect to a given type of tire, or may be developed over time based onhistorical measurements of changes in tire tread and shifts betweencorresponding modal frequencies associated with the given type of tire.

In another exemplary aspect of the aforementioned fourth embodiment, thefirst and second sets of corresponding modal frequencies are sensed viaone or more accelerometers mounted in association with the tire,responsive to excitation of structural modes for the tire. The one ormore accelerometers may be attached to the tire, for example at an innerlining of the tire, or may be mounted to a spindle of the associatedvehicle.

In another exemplary aspect of the aforementioned fourth embodiment, thetire structural modes are randomly excited during operation of the tireand associated output signals generated by the one or moreaccelerometers are captured.

In another exemplary aspect of the aforementioned fourth embodiment, thetire structural modes are excited by controlled impacting of the tirewith an external object, such as for example a hammer.

In another exemplary aspect of the aforementioned fourth embodiment, thetire structural modes are excited by directing movement of the vehiclewith respect to one or more predetermined obstacles, such as for examplea cleat or speed bump, or a course comprising a sufficiently roughsurface.

An exemplary system in accordance with the fourth embodiment asdisclosed herein may implement the vehicle tire wear estimation, forexample in view of any one or more of the previously describedembodiments and aspects thereof, via a server or server networkfunctionally linked to a data storage network and one or more sensorsmounted on the tire and/or the vehicle.

Additional advantageous features are further realized in an exemplaryvariant on the above-referenced first embodiment, wherein a fifthembodiment of the computer-implemented method is disclosed herein forestimating vehicle tire wear. The method of the fifth embodimentincludes one or more sensors, associated with a vehicle and/or at leastone tire of a plurality of tires supporting the vehicle, generatingfirst data corresponding to real time kinetics of the vehicle and/or theat least one tire. The first data is locally processed to generatesecond data as a reduced subset of the first data, wherein the seconddata is representative of the first data and comprises any one or morepredetermined features extracted therefrom. The second data isselectively transmitted to a remote computing system via acommunications network, and the remote computing system processes thesecond data and the any one or more extracted features to estimate awear characteristic for the at least one tire.

The second data may comprise a plurality of sequential data frames, eachdata frame comprising a multidimensional histogram of forces associatedwith the vehicle and/or the at least one tire.

In one exemplary aspect of the aforementioned fifth embodiment, themethod further comprises selecting a subset of the data frames betweenat least first and second events and summarizing the data frames over aparticular time or a particular distance.

In another exemplary aspect of this fifth embodiment, the summarizing ofthe data frames is performed via local processing prior to transmittalof the summarized data frames to the remote computing system.Alternatively, the subset of the data frames may be transmitted to theremote computing system wherein the summarizing of the data frames isperformed via the remote computing system.

In another exemplary aspect of this fifth embodiment, the method furthercomprises correcting for missing data in a summarized data frame byscaling the summarized data frame by an expected number of data frameswith respect to an actual collected number of data frames.

The extracted features of the second data may comprise wear performancecharacteristics representative of vehicle driving behavior.

Processing the first data may comprise performing a Fourier transform onthe first data and generating the second data comprising extractedrelevant frequencies and associated amplitudes.

In another exemplary aspect of the fifth embodiment, the second datacomprises aggregated low frequency CAN data corresponding to an amountof time spent by the vehicle in each of one or more representativedriving conditions.

In another aspect of the fifth embodiment, the first data comprises CANbus signals. The second data is generated via an encoding neural networklayer, the third data is generated via a decoding neural network layer,and a wear calculation layer is appended to the output of the decodingneural network layer and configured to transform the decoded CAN bussignals into instantaneous estimated wear values for the at least onetire.

In one exemplary aspect of the aforementioned fifth embodiment, themethod further comprises comparing the estimated wear values to actualwear values for the at least one tire to generate an error value, andproviding the error value as feedback to the neural network layers.

In another aspect of the fifth embodiment, the selective transmittal ofsecond data is automated and event-based rather than relying upon manualselection for transmittal. Alternatively, the selective transmittal ofsecond data may be time-based.

In another aspect of the fifth embodiment, a method for estimatingvehicle tire wear is implemented using one or more sensors associatedwith a vehicle and/or at least one tire of a plurality of tiressupporting the vehicle, wherein first data is generated corresponding toreal-time kinetics of the vehicle and/or the at least one tire. Via aglobal positioning system transceiver, low frequency second data isgenerated corresponding to vehicle positions. The second data isselectively transmitted to a remote computing system via acommunications network, wherein the second data is processed further inview of a vehicle model and one or more vehicle route characteristics togenerate third data corresponding to the first data, and the third datais further processed to estimate the wear characteristic for the atleast one tire.

In one exemplary aspect of the aforementioned fifth embodiment, thesecond data further comprises a plurality of sequential data frames,each data frame comprising a multidimensional histogram of forcesassociated with the vehicle and/or the at least one tire remotecomputing system. The remote computing system reconstructs a vehicleroute from the collected vehicle position data and provides vehicleroute feedback into the respective multidimensional histograms.

Additional advantageous features are further realized in an exemplaryvariant on the above-referenced first embodiment, wherein a sixthembodiment of the computer-implemented method is disclosed herein forestimating vehicle tire wear. First data is generated via one or moresensors associated with a vehicle and/or at least one tire of aplurality of tires supporting the vehicle, the first data correspondingto real-time kinetics of the vehicle and/or the at least one tire. Thefirst data is processed, via a computing system onboard the vehicle, togenerate second data as a reduced subset of the first data, said seconddata representative of the first data and comprising any one or morepredetermined features extracted therefrom. The onboard computing systemfurther processes the second data to estimate a wear characteristic forthe at least one tire, and generates a notification associated with theestimated wear characteristic to a computing device associated with avehicle user.

In one exemplary aspect of the aforementioned sixth embodiment, the stepof processing the second data to estimate the wear characteristic forthe at least one tire comprises processing the second data to generatethird data corresponding to the first data, and further processing thethird data to estimate the wear characteristic for the at least onetire.

In another exemplary aspect of the aforementioned sixth embodiment, thefirst data comprises CAN bus signals, the second data is generated viaan encoding neural network layer, the third data is generated via adecoding neural network layer, and a wear calculation layer is appendedto the output of the decoding neural network layer and configured totransform the decoded CAN bus signals into instantaneous estimated wearvalues for the at least one tire.

Another exemplary aspect of the aforementioned sixth embodiment furthercomprises comparing the estimated wear values to actual wear values forthe at least one tire to generate an error value, and providing theerror value as feedback to the neural network layers.

Additional advantageous features are further realized in an exemplaryvariant on any one of the above-referenced first through sixthembodiments, wherein a seventh embodiment of the computer-implementedmethod is disclosed herein for estimating and applying vehicle tiretraction status. A method according to the seventh embodiment mayinclude the collecting of vehicle data (e.g., comprising movement dataand location data) in association with a first vehicle, and determininga tire wear status for at least one tire associated with the vehicle.One or more tire traction characteristics for the at least one tire arepredicted, based at least on the transmitted vehicle data and thedetermined tire wear status, and one or more vehicle operation settingsare selectively modified based on at least the predicted one or moretire traction characteristics.

In one exemplary aspect of the above-referenced seventh embodiment, amaximum speed for the vehicle is determined based on at least on thetransmitted vehicle data and a determined tire wear status for each tireassociated with the vehicle.

In another exemplary aspect of the above-referenced seventh embodiment,the maximum speed is provided to an autonomous vehicle control systemassociated with the vehicle. Alternatively, the maximum speed may beprovided to a driver assistance interface associated with the vehicle.

In another exemplary aspect of the above-referenced seventh embodiment,one or more tire wear input values are received from a user via a userinterface.

In another exemplary aspect of the above-referenced seventh embodiment,the step of determining the tire wear status comprises receiving one ormore tire wear input values generated by one or more sensors mounted inor on a respective tire of the at least one tire. Alternatively, the oneor more tire wear input values may be generated by a sensor external tothe vehicle.

In another exemplary aspect of the above-referenced seventh embodiment,the step of determining the tire wear status comprises predicting one ormore tire wear input values based on at least the transmitted vehicledata and on tire data generated by one or more sensors mounted in or ona respective tire of the at least one tire.

A system may be provided for performing the method according to theabove-referenced seventh embodiment and optionally further according tocertain of the exemplary aspects, the system comprising a remote serverfunctionally linked to the vehicle via a communications network, whereinthe vehicle data is transmitted from the vehicle to the remote server.The remote server is configured to provide the one or more predictedtire traction characteristics to an active safety unit associated withthe vehicle, and the active safety unit is configured to modify the oneor more vehicle operation settings based on at least the predicted oneor more tire traction characteristics.

In an exemplary aspect of the system according to the seventhembodiment, the active safety unit may comprise an automated brakingsystem associated with the vehicle, and the remote server is configuredto provide one or more parameters of a predicted mu-slip curveassociated with a respective tire to the automated braking system.

In another exemplary aspect of the system according to the seventhembodiment, a user interface is associated with the remote server andconfigured to receive one or more tire wear input values from a user.

In another exemplary aspect of the system according to the seventhembodiment, the remote server is configured to determine a maximum speedfor the vehicle based on at least on the transmitted vehicle data and adetermined tire wear status for each tire associated with the vehicle,and provide the maximum speed to a driver assistance interfaceassociated with the vehicle.

In other exemplary aspects of the system according to the seventhembodiment, the active safety unit may comprise a collision avoidancesystem and/or an autonomous vehicle control system.

Another example of a system may perform the method according to theseventh embodiment as described above, for each of a plurality ofvehicles, and optionally further according to certain of the exemplaryaspects associated therewith. This system comprises a first remoteserver functionally linked to the vehicle via a communications network,a fleet management server functionally linked to the first remoteserver, and a vehicle control system associated with each of a pluralityof vehicles. For each of the plurality of vehicles, vehicle data istransmitted from the respective vehicle to the remote server, the firstremote server is configured to provide the one or more predicted tiretraction characteristics to the fleet management server, and the fleetmanagement server is configured to interact with the respective vehiclecontrol system for modifying the one or more vehicle operation settingsbased on at least the predicted one or more tire tractioncharacteristics.

In one exemplary aspect of this system, a user interface is associatedwith the remote server and/or the fleet management server and/or thevehicle control system, and configured to receive one or more tire wearinput values from a user.

In another exemplary aspect of this system, the fleet management serveris configured to determine a maximum speed for a given vehicle based onat least on the transmitted vehicle data and a determined tire wearstatus for each tire associated with the respective vehicle, and providethe maximum speed to the vehicle control system associated with thevehicle.

In another exemplary aspect of this system, the fleet management serveris configured to calculate a stopping distance potential for a givenvehicle based on at least on the transmitted vehicle data and adetermined tire wear status for each tire associated with the vehicle,and provide the stopping distance potential to the vehicle controlsystem associated with the vehicle.

In another exemplary aspect of this system, the fleet management serveris further configured to determine an optimal following distance foreach of a plurality of vehicles associated with a platoon of vehiclestravelling in sequence, and transmit the determined optimal followingdistance for each one of the plurality of vehicles to the respectivevehicle control system.

In another exemplary aspect of this system, the fleet management serveris configured to determine a maximum speed and/or stopping distancepotential for a given vehicle based on at least on the transmittedvehicle data and a determined tire wear status for each tire associatedwith the respective vehicle, determine whether the vehicle satisfiesthreshold traction characteristics, and interact with the vehiclecontrol system to prevent deployment of, or otherwise remove from use,the respective vehicle if the vehicle does not satisfy the thresholdtraction characteristics.

Various ones of the above-referenced embodiments may be readilycombinable with each other in a system and/or method as disclosedherein.

For example, one of skill may appreciate that a predicted tire wearaccording to the third embodiment or the fourth embodiment may beprovided as an output to a traction model according to the seventhembodiment, complementary to each other without altering the scope ofthe respective steps or features.

Further, one of skill in the art may appreciate that extracted dataaccording to the fifth embodiment may be provided as input to tire wearmodels according to one or more other embodiments as disclosed herein.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

Hereinafter, embodiments of the invention are illustrated in more detailwith reference to the drawings.

FIG. 1 is a block diagram representing an exemplary embodiment of asystem according to various embodiments as disclosed herein.

FIG. 2 is a block diagram representing an exemplary traction estimationmodel.

FIG. 3 is a graphical diagram representing an exemplary set of tractioncharacteristics produced by a model as disclosed herein.

FIG. 4 is a graphical diagram representing another exemplary set oftraction characteristics produced by a model as disclosed herein.

FIG. 5 is a graphical diagram representing an exemplary set of tire wear(e.g., tread) status values for an autonomous car fleet.

FIG. 6 is a graphical diagram representing an exemplary application fortire wear (e.g., tread) status values and predicted tire traction valuesfor a truck platoon.

FIG. 7 is a graphical diagram illustrating the effect of signalresolution on wear rate estimations, using signals collected during amix of city and highway vehicle routes.

FIG. 8 is a graphical diagram illustrating the effect of signalresolution on wear rate estimations, using signals collected primarilyduring city routes.

FIG. 9 is a graphical diagram representing an exemplary process forvehicle kinetics data aggregation and compression into histogram dataframes.

FIG. 10 is a graphical diagram representing an exemplary histogram dataframe according to the process of FIG. 9.

FIG. 11 is a graphical diagram representing an exemplary process forhistogram data summarization.

FIG. 12 is a graphical diagram representing an exemplary process forhistogram data frame scaling to correct for missing or incomplete data.

FIG. 13 is a graphical diagram representing an exemplary tire wearmodeling stream.

FIG. 14 is a graphical diagram representing an exemplary real-time modelintegration according to the tire wear modeling stream of FIG. 13.

FIG. 15 is a graphical diagram representing an exemplary neural networkautoencoder application to tire wear.

FIG. 16A is a graphical diagram representing exemplary results of neuralnetwork autoencoder compression and decompression of x-axis accelerationdata according to the example of FIG. 15.

FIG. 16B is a graphical diagram representing exemplary results of neuralnetwork autoencoder compression and decompression of y-axis accelerationdata according to the example of FIG. 15.

FIG. 16C is a graphical diagram representing exemplary results of neuralnetwork autoencoder compression and decompression of vehicle speed dataaccording to the example of FIG. 15.

FIG. 17 is a block diagram representing a traditional approach for tirewear analysis, e.g., using vehicle alignment data.

FIG. 18 is a block diagram representing an exemplary Bayesian approachfor tire wear estimation.

FIG. 19 is a graphical diagram representing an exemplary tire wear modelcorrection.

FIG. 20 is a graphical diagram representing an exemplary application ofa Monte Carlo simulation to build a set of toe angle distributions.

FIG. 21 is a graphical diagram representing an exemplary application ofa Monte Carlo simulation to build a set of camber angle distributions.

FIG. 22 is a graphical diagram representing an exemplary set of wearprogression curves for a front tire.

FIG. 23 is a graphical diagram representing an exemplary set of wearprogression curves for a rear tire.

FIG. 24 is a graphical diagram representing an exemplary brush model forwear output.

FIG. 25 is a graphical diagram representing an exemplary tire wear modelprediction, in contrast to measured data.

FIG. 26 is a graphical diagram representing the difference between tirewear model predictions as disclosed herein and various indoor wear testresults for the same control tire.

FIG. 27 is a graphical diagram representing exemplary results of staticnatural frequency tests for a given tire at new and worn states.

FIG. 28a is a graphical diagram representing exemplary results from acleat impact simulation for a tire in a new state.

FIG. 28b is a graphical diagram representing exemplary results from acleat impact simulation for the tire in a worn state.

FIG. 29 is a graphical diagram representing exemplary results for a tirein both new and worn states from a transmissibility test.

DETAILED DESCRIPTION

Referring generally to FIGS. 1-29, various exemplary embodiments of aninvention may now be described in detail. Where the various figures maydescribe embodiments sharing various common elements and features withother embodiments, similar elements and features are given the samereference numerals and redundant description thereof may be omittedbelow.

Various embodiments of a system as disclosed herein may includecentralized computing nodes (e.g., a cloud server) in functionalcommunication with a plurality of distributed data collectors andcomputing nodes (e.g., associated with individual vehicles) foreffectively implementing wear and traction models as disclosed herein.Referring initially to FIG. 1, an exemplary embodiment of the system 100includes a computing device 102 that is onboard a vehicle and configuredto at least obtain data and transmit said data to a remote server 130and/or perform relevant computations as disclosed herein. The computingdevice may be portable or otherwise modular as part of a distributedvehicle data collection and control system (as shown), or otherwise maybe integrally provided with respect to a central vehicle data collectioncontrol system (not shown). The device may include a processor 104 andmemory 106 having program logic 108 residing thereon. Generally stated,a system as disclosed herein may implement numerous componentsdistributed across one or more vehicles, for example but not necessarilyassociated with a fleet management entity, and further a central serveror server network in functional communication with each of the vehiclesvia a communications network. The vehicle components may typicallyinclude one or more sensors such as, e.g., vehicle body accelerometers,gyroscopes, inertial measurement units (IMU), position sensors such asglobal positioning system (GPS) transponders 112, tire pressuremonitoring system (TPMS) sensor transmitters 118 and associated onboardreceivers, or the like, as linked for example to a controller areanetwork (CAN) bus network and providing signals thereby to localprocessing units. The illustrated embodiment includes for illustrativepurposes, without otherwise limiting the scope of the present inventionthereby, an ambient temperature sensor 116, an engine sensor 114configured for example to provide sensed barometric pressure signals,and a DC power source 110.

In view of the following discussion, other sensors for collecting andtransmitting vehicle data such as pertaining to velocity, acceleration,braking characteristics, or the like will become sufficiently apparentto one of ordinary skill in the art and are not further discussedherein. Various bus interfaces, protocols, and associated networks arewell known in the art for the communication of vehicle kinetics data orthe like between the respective data source and the local computingdevice, and one of skill in the art would recognize a wide range of suchtools and means for implementing the same.

The system may include additional distributed program logic such as forexample residing on a fleet management server or other computing device140, or a user interface of a device resident to the vehicle orassociated with a driver thereof (not shown) for real-time notifications(e.g., via a visual and/or audio indicator), with the fleet managementdevice in some embodiments being functionally linked to the onboarddevice via a communications network. System programming information mayfor example be provided on-board by the driver or from a fleet manager.

Vehicle and tire sensors may in an embodiment further be provided withunique identifiers, wherein the onboard device processor 104 candistinguish between signals provided from respective sensors on the samevehicle, and further in certain embodiments wherein a central server 130and/or fleet maintenance supervisor client device 140 may distinguishbetween signals provided from tires and associated vehicle and/or tiresensors across a plurality of vehicles. In other words, sensor outputvalues may in various embodiments be associated with a particular tire,a particular vehicle, and/or a particular tire-vehicle system for thepurposes of onboard or remote/downstream data storage and implementationfor calculations as disclosed herein. The onboard device processor maycommunicate directly with the hosted server as shown in FIG. 1, oralternatively the driver's mobile device or truck-mounted computingdevice may be configured to receive and process/transmit onboard deviceoutput data to the hosted server and/or fleet management server/device.

Signals received from a particular vehicle and/or tire sensor may bestored in onboard device memory, or an equivalent data storage unitfunctionally linked to the onboard device processor, for selectiveretrieval as needed for calculations according to the method disclosedherein. In some embodiments, raw data signals from the various signalsmay be communicated substantially in real time from the vehicle to theserver. Alternatively, particularly in view of the inherentinefficiencies in continuous data transmission of high frequency data,the data may for example be compiled, encoded, and/or summarized formore efficient (e.g., periodic time-based or alternatively definedevent-based) transmission from the vehicle to the remote server via anappropriate communications network.

The vehicle data and/or tire data, once transmitted via a communicationsnetwork to the hosted server 130, may be stored for example in adatabase 132 associated therewith. The server may include or otherwisebe associated with tire wear models and tire traction models 134 forselectively retrieving and processing the vehicle data and/or tire dataas appropriate inputs. The models may be implemented at least in partvia execution of a processor, enabling selective retrieval of thevehicle data and/or tire data and further in electronic communicationfor the input of any additional data or algorithms from a database,lookup table, or the like that is stored in association with the server.

In an embodiment of a method as disclosed herein, a system 100 asdescribed above may be implemented for modeling and predicting of tireperformance and the provision of feedback based thereon. The method mayinclude collecting vehicle data comprising movement data and/or locationdata for a vehicle and/or at least one tire associated with the vehicle,and determining a current tire wear status in real-time for the at leastone tire, based at least in part on the collected data. One or more tireperformance characteristics are predicted, based at least in part on thedetermined tire wear status and the collected data. Real-time feedbackis selectively provided, based on the predicted one or more tireperformance characteristics and/or determined current tire wear status.In various embodiments as disclosed herein, some or all of these stepsmay be expanded upon as discussed below to provide further advantages.

For example, referring next to FIG. 2, an embodiment of a system andmethod as disclosed herein implements a simplified model 134B of a tirealong with the tire's wear state 150 to predict its tractioncapabilities 160, which is relayed to the user to promote safe driving.The simplified model predicts the forces and moments on the tire under agiven friction, load, inflation pressure, speed, etc. The terms “tirewear” and “tread wear” may be used herein interchangeably for thepurpose of illustration.

For the traction model 134B to be accurate, especially for wetconditions, the tread depth 150 must be known/estimated. This may beaccomplished by any of several exemplary techniques as follows.

In one embodiment, tire wear (tread) measurements 150 may be mademanually by the user and provided as user input into an app orequivalent interface associated with the onboard computing device 102 ordirectly with the hosted server 130. The interface may for exampleenable direct input of wear values by the user with respect to aselected tire from among a plurality of tires mounted on an identifiedvehicle. Alternatively, the interface may be configured to prompt theuser for a captured image or alternative input associated with a treadprofile, wherein the wear values may be indirectly determined from theuser input.

In another embodiment, tire wear measurements 150 may be made by atire-mounted sensor and provided to the hosted server, for examplewithout requiring input from the user. Such sensors may for example bemounted directly in the tire tread.

In another embodiment, tire wear measurements 150 may be provided viaone or more sensors external to the vehicle and sent to the cloud server130, again for example without requiring input from the user. As oneexample, the one or more sensors may include a drive-over optical sensorcomprising a laser emitter configured to capture tire tread informationby projecting laser light onto or across a surface of the tire passingover the sensor, and one or more laser receiving elements configured tocapture reflected energy and thereby acquire a profile of the tire fromwhich the tire tread may be determined.

In another embodiment, as represented for example in FIG. 2 and examplesof which are provided in various embodiments below, tire wear values 150may be estimated based on a wear model 134A. The wear model may comprise“digital twin” virtual representations of various physical parts,processes or systems wherein digital and physical data is paired andcombined with learning systems such as for example neural networks. Forexample, real data 136 from a vehicle and associated location/routeinformation may be provided to generate a digital representation of thevehicle tire for estimation of tire wear, wherein subsequent comparisonof the estimated tire wear with a determined actual tire wear may beimplemented as feedback for the machine learning algorithms. The wearmodel 134A may be implemented at the vehicle, for processing via theonboard system 102, or the tire data 138 and/or vehicle data 136 may beprocessed to provide representative data to the hosted server 130 forremote wear estimation.

The tire wear status (e.g., tread depth) 150 as shown in FIG. 2 may forexample be provided along with certain vehicle data 136 as inputs to thetraction model 134B, which may be configured to provide an estimatedtraction status 160 or one or more traction characteristics 160 for therespective tire. As with the aforementioned wear model, the tractionmodel may comprise “digital twin” virtual representations of physicalparts, processes or systems wherein digital and physical data are pairedand combined with learning systems such as for example artificial neuralnetworks. Real vehicle data 136 and/or tire data 138 from a particulartire, vehicle or tire-vehicle system may be provided throughout the lifecycle of the respective asset to generate a virtual representation ofthe vehicle tire for estimation of tire traction, wherein subsequentcomparison of the estimated tire traction with a corresponding measuredor determined actual tire traction may preferably be implemented asfeedback for machine learning algorithms executed at the server level.

The traction model 134B may in various embodiments utilize the resultsfrom prior testing, including for example stopping distance testingresults, tire traction testing results, etc., as collected with respectto numerous tire-vehicle systems and associated combinations of valuesfor input parameters (e.g., tire tread, inflation pressure, road surfacecharacteristics, vehicle speed and acceleration, slip rate and angle,normal force, braking pressure and load), wherein a tire traction outputmay be effectively predicted for a given set of current vehicle data andtire data inputs.

In one embodiment, the outputs 160 from this traction model 134B may beincorporated into an active safety system. As previously noted, data isbeing collected from sensors on the vehicle to feed into the tire wearmodel 134A which will predict tread depth 150, and this will be fed intothe traction model 134B. The term “active safety systems” as used hereinmay preferably encompass such systems as are generally known to one ofskill in the art, including but not limited to examples such ascollision avoidance systems, advanced driver-assistance systems (ADAS),anti-lock braking systems (ABS), etc., which can be configured toutilize the traction model output information 160 to achieve optimalperformance. For example, collision avoidance systems are typicallyconfigured to take evasive action, such as automatically engaging thebrakes of a host vehicle to avoid or mitigate a potential collision witha target vehicle, and enhanced information regarding the tractioncapabilities of the tires and accordingly the braking capabilities ofthe tire-vehicle system are eminently desirable.

By reference to exemplary models as shown in FIGS. 3 and 4, each graphincludes two curves representing the same hypothetical tire at differentwear levels. As can be seen, as the tire wears the wet tractionperformance deteriorates accordingly. During inclement weather, there isa critical speed for worn tires wherein the user risks hydroplaning Witha traction model linked remotely to an onboard display or equivalentuser interface, a maximum speed can be communicated to the user toprovide safer driving conditions.

Traction output information, such as for example mu-slip curves (See,e.g., FIG. 4) determined according to the respective wear states, mayalso be fed into the active safety systems for vehicle controlimplementation and thereby optimized performance. The slip ratiorepresents ((vehicle speed−tire rotation speed)/vehicle speed), whereina slip ratio of 0% corresponds to a free rolling tire and a slip ratioof 100% corresponds to a locked wheel. As the tire mu-slip curve shapesare altered over time from a “New Tire” curve to a “Worn Tire” curve asrepresented in FIG. 4, the active safety system may preferably beconfigured to determine what, if any, changes could be made to improvetire-vehicle performance characteristics. Different mu-slip curves maybe considered to possess relevant shape and location characteristicswhich influence the ability of an active safety system (e.g., the ABS)to optimize performance, wherein for example the respective peakamplitude “mu” is generally understood to influence the stoppingdistance (the higher the better). Other relevant characteristics of themu-slip curve shape may include, for example, the slip ratio at they-axis (mu) peak of the curve, the curvature at or proximate to saidpeak, the initial slope of the curve, etc.

In another embodiment, a ride-sharing autonomous fleet could use outputdata 160 from the traction model 134B to disable or otherwiseselectively remove vehicles with low tread depth from use duringinclement weather, or potentially to limit their maximum speeds. Byreference to the exemplary model as represented in FIG. 3, it may benoted that a tire with a “worn” state is identified with a hydroplaningcritical speed of ˜55 miles per hour at which a peak coefficient offriction falls below the threshold of 0.25, as compared to a tire with a“new” state which may exceed 100 miles per hour without the peakcoefficient of friction falling below the same threshold. The system mayaccordingly limit the speed of a vehicle including one or more tiresworn to such a state. If the vehicle is part of a ride-sharingautonomous fleet, and a user is seeking a ride during severe weatherconditions along a route that requires elevated minimum (e.g., highway)speeds, the system may be configured to disable deployment of vehiclesbelow a certain tread depth or otherwise having insufficient tractioncapabilities. As shown in FIG. 5, an exemplary autonomous vehicle fleetmay comprise numerous vehicles having varying minimum tread statusvalues, wherein the fleet management system may be configured to disabledeployment of vehicles falling below a minimum threshold. The system maybe configured to act upon a minimum tire tread value for each of aplurality of tires associated with a vehicle, or in an embodiment maycalculate an aggregated tread status for the plurality of tires forcomparison against a minimum threshold.

In another embodiment, a fleet management system may implement theoutput data 160 from the traction model 134B with respect to a definedplatoon of vehicles, such as to better optimize their followingdistances to achieve maximum fuel savings by better understanding eachtire's stopping distance potential. One of skill in the art mayappreciate that minimizing following distances can result in reducedaerodynamic drag for all vehicles in a platoon and thereby improverespective fuel economies, particularly where more than two trucks areincluded in the platoon, and the disclosed improvements to vehicleplatooning methods can desirably facilitate the reduction of followingdistances beyond a more conventional “one size fits all” approach. Themost fuel savings may typically be obtained at following distances ofless than ˜20 meters, a distance which may be difficult or impossible tomaintain during poor weather using conventional techniques fordetermining traction/braking capabilities. By more effectivelydetermining a safe following distance, even for inclement weatherconditions, the percentage of time spent platooning may also beincreased.

In an embodiment, the active safety or platoon following distanceinformation may be provided to a vehicle braking control system orvehicle platooning control system 120 associated with each respectivevehicle. In the context of a vehicle platoon, it may be that a singlevehicle associated with the platoon receives following distanceinformation and/or certain vehicle control information and passes alongthe information to other vehicles in the platoon via otherwiseconventional vehicle-to-vehicle communication systems and protocols. Thefollowing distance information provided by the system as disclosedherein may be considered for example a nominal or minimum effectivefollowing distance setting based on the respective traction status forvehicles in a platoon, with the understanding that the vehicleplatooning control system for a given vehicle or platoon of vehicles mayfurther alter the following distance settings based on monitored trafficevents, road conditions, and other ambient conditions that may beoutside the scope of the traction status determinations for a givenembodiment. For example, a first following distance which may beacceptable for a given vehicle under normal driving conditions maynecessarily be increased based on monitored real time events such as achange in grade of the road to be traversed, or a heightened risk ofbraking events by any one or more vehicles in the platoon.

Components of a vehicle platooning control system 120 are generallyknown in the art, and may include for example vehicle braking controlsystems, collision mitigation systems, vehicle-to-vehiclecommunications, and one or more sensors collectively configured tomonitor vehicle data such as a current following distance of the hostvehicle (with respect to another vehicle in the platoon or anon-platooning target vehicle), a respective type of said targetvehicle, a relative acceleration or deceleration value for the hostvehicle, a pressure value with respect to a braking actuator for thehost vehicle, etc.

As previously noted, various embodiments of a method may estimate tirewear values 150 based on a wear model 134A. Current wear models requireseveral inputs about the system to accurately project out the wear lifeof the tire and are developed using very high frequency data. However,transmitting high frequency data from distributed data collectors (e.g.,associated with individual vehicles) to centralized computing nodes(e.g., cloud servers) is prohibitively expensive at scale.

Referring for illustrative purposes to FIGS. 7 and 8, the data presentedtherein illustrates the effect of signal resolution on wear rateestimations. To construct these plots the source data is down sampled toreduce the resolution of the data. The down sampling in these examplesis performed by simply decimating the source data. The source data has aresolution of one meter per sample in the distance domain, or roughly 20Hz at a speed of 45 mph. The x-axis shows a range of one meter persample up to one thousand meters per sample. The y-axis shows therelative error in the wear estimation.

In both figures, the data sets respectively correspond to all four tiresof a Toyota Camry front wheel drive vehicle, using Turanza EL400All-Season tires. In FIG. 7, the data is representative of an “averageNorth American driver” on a mixture of city and highway roads, wherein alower predicted wear rate generally corresponds to a lower accuracy inwear prediction. In FIG. 8, the data is representative of a city taxifleet, wherein the vast majority of the mileage is in city drivingcontexts and a higher sampling rate is clearly required relative to theprevious data sets.

The results as shown indicate that simple down sampling of the data isnot a reliable, robust, and efficient method of reducing data storageand transmission requirements. The minimum resolution needed to achievegood prediction is strongly dependent on the route driven (e.g., citydominated or mixed city and highway) and driving style. In addition, theminimum resolution needed is also dependent on the tire's position onthe vehicle (e.g., left-front, right-front, etc.).

Therefore, one of skill in the art may appreciate the desirability ofmore complex strategies maximize vehicle kinetics data storage andtransmission efficiencies for tire wear estimation.

Exemplary tire wear models 134A as disclosed herein may summarize datafrom high frequency or alternative low frequency sources into lowfrequency data, such as route data, which can be transmitted at thislower frequency to the cloud in a cost-effective manner, enabling directwear modeling. In certain embodiments, improved efficiencies can beachieved with adaptive solutions to make the methods more robust andadaptive to field conditions, e.g., by encoding wear estimation featuresinto a compressed/reduced dataset.

In an embodiment, real-time vehicle kinetics data may be collected fromsensors on a vehicle, and then filtered and down sampled into summarizedbuckets to create a histogram of the relevant forces. For example, rawaccelerometer data may be down sampled and aggregated into a histogramthat is representative of the raw data but at a coarse level.

As represented for example in FIG. 9, the real-time vehicle kineticsdata 310 may be compiled into windows 320 of time and/or distance. Thecompiled data may further be aggregated into histogram data frames 330.The data frames 330 in the illustrated embodiment are multi-dimensionaland contain vehicle body accelerations and vehicle body speed. Eachpoint in the histogram represents the time or distance spent in thatcondition. The bins of the histogram may be optimized to maximize wearcalculation accuracy and to further minimize data storage and transfercosts, for example implementing simple equally spaced or non-linear binlayouts.

FIG. 10 illustrates an example of a histogram data frame having a firstdimension associated with lateral vehicle acceleration, and a seconddimension associated with fore-aft vehicle acceleration. The individualpoints in this example are color-coded to represent a time or distancespent in the corresponding condition.

Referring next to FIG. 11, as wear is a cumulative process it is usefulto summarize data between specific events in time and/or distance.Examples of relevant events may include without limitation: by vehicletrip, tire tread depth measurement events, tire rotation events, tiremount events, vehicle maintenance events, daily/monthly/yearlysummaries, mileage summaries (5k, 10k, 20k miles, etc. . . . ).Histogram data frames 330 allow for flexible and efficientsummarization, which can be used on static data in the cloud (aftertransfer) or on transient data on the vehicle (before the data istransferred).

Unfortunately, data from vehicle systems and communication systems areoften, or even inherently, unreliable. One of skill in the art mayappreciate the desirability of designing software systems to bepredictable and robust in cases where data is missing or corrupt. Sincewear is a cumulative process, missing data poses a problem for wearcalculations. Histogram data frames 330 as disclosed in accordance withthe present embodiment allow for efficient compensation for missingdata.

Referring next to FIG. 12, a plurality of histogram data frames 330having a missing subset of data therein may be summarized to generate apartial data frame 430, which may further be corrected by scaling thedata frame by the expected number of data frames with respect to thecollected number data frames. The result (corrected data frame 440) willbe an average of the driver's behavior.

As previously noted, and with further reference now to a tire wearmodeling stream as represented in FIG. 13 and an exemplary real-timemodel integration as represented in FIG. 14, vehicle kinetics seriesdata 710 may be acquired using one or more sensors on or associated withthe vehicle. A real-time vehicle to tire model 720 can then be used tosimulate tire forces on each tire. Furthermore, a model of the tire canbe utilized to produce wear rate simulations 730. Both such models canbe implemented either in real-time on time/distance series data or onthe aggregated data frames. Simulation results of the model can bestored or transmitted in data frame form.

The example in FIG. 14 shows real-time simulation of tire forces andtransmission of tire force data frames 830. The scope of the presentembodiment is not necessarily limited thereto, and one of skill in theart may appreciate alternative strategies for various use cases.

It should be noted that whereas numerous embodiments as disclosed hereinsimulate forces on each tire based on vehicle kinetics data, the scopeof the invention is not limited thereto unless otherwise specificallystated. In other words, it is within the scope of the invention toprovide raw data corresponding to one or more forces applied to at leastone tire if such data is available in a given application.

In another embodiment of a method as disclosed herein, the vehiclekinetics data may be filtered, down sampled and aggregated into a subsetof behavioral or “driver severity” values that are representative of howthe vehicle is driven. These values are extracted from the raw data tospecifically capture predetermined wear performance characteristics ofthe driver's behavior. The extracted behavioral features are furtherprocessed by the downstream (e.g., host server-based) wear model.Behavioral values as features extracted from the raw data prior totransmittal into the cloud may optionally supplement or otherwisecomplement other forms of summarized or compressed data in accordancewith other embodiments as disclosed herein.

In another embodiment, low frequency GPS data from the vehicle may betransmitted to the cloud server, wherein the route is reconstructed witha reverse mapping algorithm and fed into a time series histogram tounderstand the time spent in various driving conditions (highway,turning, braking, etc.). As with the aforementioned embodiment, vehicleposition data collected or extracted prior to transmittal into the cloudmay optionally supplement or otherwise complement other forms ofsummarized or compressed data in accordance with other embodiments asdisclosed herein.

In another embodiment, low frequency CAN data may be aggregated to countthe time spent in various driving conditions that is used to calculatewear state. As with the two previous embodiments, feature extraction inthe form of event-based driving detection prior to transmittal into thecloud may optionally supplement or otherwise complement other forms ofsummarized or compressed data in accordance with one or more otherembodiments as disclosed herein.

In another embodiment, with further reference now to FIG. 15, a neuralnetwork autoencoder 900 may be implemented to transform and compress theinput CAN bus signals 910 in a first (i.e., encoder) layer 920, andafter transmittal of the compressed data into the cloud to furtherreconstruct the data in a second (i.e., decoder) layer 940 for use bytire wear models to predict tire performance. As further illustrated inthree graphical diagrams, a first vehicle acceleration data stream(x-axis acceleration as shown in FIG. 16A), a second vehicleacceleration data stream (y-axis acceleration as shown in FIG. 16B) anda vehicle speed data stream (as shown in FIG. 16C) can be compressed andreconstructed to their respective original signals with very highaccuracy. In each diagram, the raw data and the reconstructed data areoverlaid to highlight this accuracy.

Neural network autoencoders 900 are well known in the art forimplementing reductions in data dimensionality, and typically comprisenumerous pairs of layers. An input layer 910 has a first size, which isreduced via encoding layer 920 with subsequent layers until a middlelayer 930 is reached, after which the layer sizes increase via decoding940 until an output layer 950 having the first size. An exemplary use ofan autoencoder as disclosed herein may vary from the conventionalarrangement in that it further includes a specialized third (i.e., wearestimation) layer 960 that is designed and appended to the second layer950. The specialized third layer 960 is configured to implement wearrate calculations to transform raw CAN bus signals into an instantaneous(actual) wear rate 970. For example, the wear layer may compriseproprietary equations containing specific vehicle and tire informationrelating to the physical system. Because the original vehicle kineticsdata signals can be reconstructed with very high accuracy via the firstand second layers of the neural network, the additional third (wearspecific) layer can similarly be highly accurate.

This third layer 960 further may enable the first (encoding) layer 920and second (decoding) layer 940 to be specifically trained over time forestimating wear. During the training process the encoding and decodinglayers learn to capture and store the most essential information forwear calculations. For example, an estimated instantaneous wear rate orpredicted wear rate can be compared against an actual wear rate togenerate a model error value 980. A feedback loop 990 provides the modelerror values back to the autoencoder for updating of model weights andbiases in the first layer 920 and/or second layer 940. The third layer960 will propagate through weights specific to estimating or predictingtire wear.

Otherwise stated, appending the third layer 960 to the end of aconventional autoencoder (i.e., after the second layer 940) allows theneural network to learn a representation of how to best transform theCAN bus signals to be used for predicting tire wear, whereas aconventional autoencoder would simply learn the best representation fordirect regurgitation of the original signals. With an improved encodinglayer, as learned over time via for example the aforementioned feedbacksystem, the data is encoded in a manner that enables the decoding layerto produce optimal signals for estimating or predicting tire wear.

This network architecture may enable the network to learn the physicallymost important signal features and patterns (peaks, valleys,cross-signal relationships, etc.) for wear and efficiently propagatethose features through the network.

In another embodiment, the system may be configured to run a Fouriertransform on the raw data stream and to extract the most relevantfrequencies. These frequencies and accompanying amplitudes may furtherbe used after transmission to the cloud to reconstruct the full raw datastate.

Another exemplary embodiment as disclosed herein, further by referenceto FIGS. 17-23, relates to the use of Bayesian methods in thecharacterization and prediction of tire wear. The foundation of thisapproach is the representation of factors contributing to wear (such asdriving style, vehicle alignment settings, routes, road surfaces,environmental conditions, tire manufacturing variability, etc.) asprobability distributions. The rationale behind representing these asprobability distributions is that the variations observed in each ofthese factors are not noise, but truly represent the natural variationthat is observed with regards to wear. For example, the same tire beingused by an aggressive first driver (who accelerates and brakes hard),relative to a second driver who is more careful, would experience verydifferent tire wear life span. An average representation of these twodrivers, when used with a conventional prediction model, would produce aprediction that is insufficient when applied for either caseindividually.

The effect of such a probabilistic representation of the contributingfactors is that the predictions made by a wear algorithm will also beprobabilistic, i.e., the prediction is also a distribution. There areseveral benefits for using distributions when reporting the prediction.First, predictions can carry a measure of uncertainty with them i.e.tread wear is 4.1 mm +/−0.05 mm or wear out prediction is 55,000 miles+/−3000 miles (both ranges could correspond to specific confidencelevels, such as 95% or 98%). Second, Bayesian inference can be used toupdate these distributions based on observations. Such observationscould for example be on the predicted variables (e.g., measurement oftread depth) or input variables (accelerations characterizing drivingstyle). The value of this inference may be in that the model or anassociated system as further described below can continue updating theprediction, as well as the confidence in such predictions, over timewith respect to for example a specified distance traveled or a timespent traveling using the associated tire(s).

Referring to the schematic in FIG. 18, an exemplary process flow may beillustrated by way of comparison to a traditional approach asrepresented in FIG. 17. Probabilistic distributions of factors such asvehicle wheel and suspension settings relating to tire wear can begenerated and fed into a vehicle model, as opposed to specific target ormeasured values for the same factors. One example of such a factor isthe camber angle, otherwise known as the angle from the normal of a roadsurface through the center of a respective wheel (upon which the tire ismounted) to a center line of the wheel. Another example of such a factoris toe angle, otherwise known as the angle of the tire with respect tothe longitudinal axis of the respective vehicle.

From these initial ranges, further probabilistic distributions may begenerated regarding or otherwise corresponding to each of a plurality ofrelevant forces (e.g., a tractive or longitudinal force Fx, a lateralforce Fy, a vertical or normal force Fz) and/or moments (e.g.,overturning torque My, aligning torque Mz) on an associated tire, againas opposed to individual values for the same forces. The forcedistributions may be fed into a tire wear model wherein tread depth isestimated for a given distance traveled (e.g., 15000 km) according to abaseline value (e.g., 5.8 mm) with a calculated range of uncertainty(e.g., +/−0.3 mm) as opposed to the baseline value alone.

The probability distribution for the tread depth, as shown in theschematic above, can subsequently be updated based on observations. Thisupdate may be implemented using a representation of the Bayes theoremwhich is shown here:

$\underset{\underset{posterior}{︸}}{P\left( {{model}❘{observations}} \right)} \propto {\underset{\underset{likelihood}{︸}}{P\left( {{observations}❘{model}} \right)} \times \underset{\underset{prior}{︸}}{P({model})}}$

Bayesian filtering approaches are known in the art to determine thelikelihood of a given measurement in view of, e.g., all previouscorresponding measurements in a sensor data stream. Here, the term“model” refers to the parameters of the model and the term“observations” denotes the measurements made on any/all variablesinvolved in the model. According to the aforementioned equation,information relating to the tire wear predictions can be updated overtime using actual measurements. In other words, using this approach wecan “correct” the model prediction with every measurement that is takenof a particular tire element and/or vehicle-tire system. For example, iftread depth measurements are periodically collected and transmitted orotherwise compiled for application according to a system and method asdisclosed herein, such measurements can be implemented to reduce theuncertainty and enable better predictions over time.

Referring next to FIG. 19, corrections in tear wear prediction may beprovided periodically along with tread depth measurements, wherein anuncertainty in the wear prediction is correspondingly reduced. Asmeasurements of tread depth (or an equivalent tire wear-related factor)are collected over time, potential alternative models or time-seriescurves may be effectively ruled out or minimized in relevance withrespect to a given tire, vehicle-driver-tire system, or the like, andsubsequent tire wear estimations may be more accurately provided andwith less uncertainty in their respective outcome.

As illustrated, a wear prediction curve proceeds from a first point(along the y-axis) with a surrounding wear prediction uncertainty U0.After a subsequent tread depth measurement, a corrected wear predictioncurve is generated along with a reduced level of uncertainty U1 in thewear prediction. In this example, the second envelope of uncertainty U1falls entirely within the first envelope. After another tread depthmeasurement, a third and further corrected wear prediction curve isgenerated, along with a still further reduced level of uncertainty U2 inthe wear prediction.

Referring next to FIGS. 20 and 21, an exemplary application of a MonteCarlo approach is represented to build probabilistic distributions anduse those distributions to generate a distribution of wear progressioncurves (see, e.g., a wear progression curve for an exemplary front tireas shown in FIG. 22 and a wear progression curve for an exemplary reartire as shown in FIG. 23). In other words, for a given variation invehicle alignment settings, the approach seeks to determine acorresponding variation in wear progression. In the particularillustrated case, the inputs were assumed to be independent normaldistributions only for the toe angle and the camber angle, whereineverything else is known as single points. Although toe angle and camberangle have been selected for illustration herein, it may be understoodthat alternative or additional vehicle and/or tire settings may besuitable for the tire wear models and accordingly implemented by thesystems and methods as disclosed herein, unless otherwise specificallynoted.

Referring in particular to the rear tire progression curves in FIG. 23,a central curve represents a nominal toe angle/camber angle setting,wherein the surrounding region represents ten thousand individual wearprogression curves corresponding to respective initial wear rates Ew. Asmay be observed, with increasing mileage the variation in wearprogression also correspondingly increases. By implementing periodicmeasurements of values for the underlying factors, an appropriate subsetof the individual wear progression curves can be identified withincreasing certainty over time, wherein the tire wear status can beaccurately predicted with only a relatively small number of actualmeasurements.

Accordingly, even periodic measurements of the tread depth or otherrelevant factors provide real time feedback to users (e.g., fleetmanagers, end-users) and enhance the ability to predict the wear lifeleft in the tire and further maximize the remaining value in the tire.

Periodic measurements associated with tire wear (e.g., tire tread depth)for supplementing the probabilistic distributions may be made directly(manually by users and/or via one or more sensors), and/or estimated inaccordance with tire wear models and techniques as otherwise describedherein.

Another exemplary embodiment of a method as disclosed herein, further byreference to FIGS. 24-26, relates to the use of brush-type analytics inthe characterization and prediction of tire wear. A brush-type model isa simplified tire model with a logical physical background that modelsthe tread elements as independent “brush bristles” extending outwardfrom a base material of the tire (e.g., carcass). The brush-type modelgreatly reduces the complexity of modeling a contact interface betweenthe road surface and the base material, wherein the modeled treadelements can deform in various measurable directions (e.g.,longitudinal, lateral, vertical) and can capture the first order effects(tread block stiffening and contact area increasing) that occur in areal tire as it wears. In alternative embodiments, the characterizationand prediction of tire wear can be implemented using other physics basedtire wear models, such as for example finite element analysis (FEA).

An embodiment of the method as disclosed herein further advantageouslypredicts the absolute wear rate of the tire under a given condition,rather than merely predicting how the wear rate changes as tread depthdecreases. This is accomplished at least in part by normalizing acurrent normalizing the modeled wear rate (e.g., based on periodic orotherwise updated measurements) with respect to the wear rate at theoriginal tread depth (i.e., initial wear rate).

Referring for example to the graphical diagram in FIG. 24, an exemplaryoutput of the model is illustrated with the normalized wear rate ratioon the y-axis, and the tread loss on the x-axis for two different tires.An initial wear rate may be provided as an input to the system, forexample but without limitation being provided from an FEA stage, amachine learning model, or the like, the tread depth progression for theentire life of a give tire can be predicted.

Referring next to FIG. 25, an exemplary set of results are illustratedwhen using this predictive method to simulate a wear reference tirecompared with measured data of the same tire/vehicle/vehicle-tire systemvia outdoor wear testing. The circular markers indicate the mean treaddepth of the control tire test results at each inspection mileage,whereas the underlying solid line represents the predicted tread depthas normalized with respect to an initial tread depth and further via thebrush-type model.

Validation data as further represented in FIG. 26 further indicates anacceptable model fit for an exemplary tire wear model such as a hybridbrush-type model as disclosed herein. In this case, the differencebetween prediction results and indoor wear test results of a certaincontrol tire was less than 0.25 mm for each mileage for which the treaddepth was inspected.

A hybrid brush-type model as disclosed herein is extremely fast andefficient and can be executed substantially in real-time. The testresults to date show that the model is accurately predicting wearprogression for very different tire designs. Only a relatively smallsubset of inputs is needed, such as for example the original tread depthand the contact/void area at various tread depths. This information canbe taken from, e.g., 3D models of the tread pattern, or from acircumferential tread wear imaging system (CTWIST) measurement of atire, which is typically provided for every tire tested for indoor oroutdoor wear.

In an embodiment, other tire-related threshold events can be predictedand implemented for alerts and/or interventions within the scope of thepresent disclosure. For example, the system can identify other servicesthat are recommended for a given vehicle based on time-series inputsreceived and processed as described above, predicted tire wear, and thelike. Examples of such services may include without limitation tirerotation, alignment, inflation, and the like. The system may generatethe alerts and/or intervention recommendations based on individualthresholds, groups of thresholds, and/or non-threshold algorithmiccomparisons with respect to predetermined parameters.

In an embodiment, an optimal type of tire and/or tire parameter can bepredicted and implemented for alerts and/or interventions within thescope of the present disclosure. For example, the system can identifyvehicle applications (higher instances of city driving, higher instancesof highway driving, etc.) and/or driving styles based at least in parton the time-series inputs received and processed as described above,predicted tire wear, and the like. The system may determine that certaintires are more appropriate for a given vehicle based not only on thetype of vehicle but also on the identified vehicle applications and/ordriving styles, and further generate the alerts and/or interventionrecommendations based at least in part thereon.

As previously noted, tire information may be provided from one or moresensors mounted on a given tire or an associated vehicle. The one ormore sensors may be accelerometers mounted directly on, e.g., an innerlining of the tire or a vehicle spindle. Output signals from the sensorsmay be provided to the hosted server, for example without requiringinput from the user.

Referring more particularly now to FIGS. 27-29, another exemplarytechnique is disclosed herein for estimating the tread depth of a tire.One of skill in the art may appreciate that as the tire wears and losesmass, the modal frequencies change in a manner that either directlyrelates or can be correlated to the mass loss. This principle is clearwhen considering a one-degree-of-freedom mass-spring system, where thenatural frequency is equal to the square root of the spring stiffnessdivided by the mass. As the mass decreases, the natural frequencyincreases. Using this same principle for structural modes of the tire,the mass loss can be determined based on the modal frequency shift by:

${\Delta\; m} = {m\left( {1 - \left( \frac{\omega_{n,{new}}}{\omega_{n,{worn}}} \right)^{2}} \right)}$

where Δm is the mass change, m is the mass when new, and con is thenatural frequency.

The modal frequencies can be identified by several methods, including(as previously noted) having an accelerometer attached to the tire, orhaving an accelerometer attached to the spindle of the vehicle. The tirestructural modes may also be excited in various manners, including forexample a controlled impacting of the tire with an object (such as ahammer, kicking the tire, etc.), electric excitation, running over anobstacle (such as a cleat or speed bump) and/or running the vehicle-tirecombination over a rough surface. In certain embodiments, randomexcitation events may take place during operation of the vehicle-tirecombination, wherein output signals from the sensors may be collectedand stored, and/or processed to estimate tire wear.

FIG. 27 illustrates an example from a static natural frequency testwhere a given tire was impacted by a hammer, and an accelerometer wasattached to the inner liner of the tire. The vibrations associated withthe impact produce output signals from the accelerometer with PowerSpectral Density (PSD) waveforms as shown. The PSD waveform for a givenimpact represents the frequency distribution for an associated outputsignal. An accelerometer may be configured to provide an output voltagewhich may be converted by signal processing circuitry into equivalentacceleration signals. These time domain signals may themselves befurther transformed into the frequency domain using, e.g., Fast FourierTransformation. The frequency response functions in the power spectrummay typically contain magnitude information, represented in decibel (dB)scale.

Corresponding peaks in the frequency spectrum from the respectivewaveforms for new and worn states of the given tire are highlighted toillustrate the frequency shift due to tread loss there between. In thisexample, the mass loss calculated from the equation above was 0.474kilograms (kg), substantially identical to that of an actual measuredvalue of 0.467 kg. In various embodiments, an additional step may beimplemented to relate the mass loss to the tread loss, or alternativelyit may be more reliable to perform a correlation of modal frequencyshift with respect to tread depth for a given tire.

Finite Element Analysis (FEA) simulations have also been performed thatshow a similar frequency shift from, e.g., both transmissibility tests(where the base is excited by a random input) and cleat impact (wherethe tire rolls over a cleat).

FIG. 28A represents results from a cleat impact simulation for a newtire, with a first graph illustrating vertical force variations withrespect to time and a second graph illustrating Fast Fourier Transform(FFT) magnitude with respect to a range of frequencies (in Hz).

FIG. 28B represents corresponding results from a cleat impact simulationfor the same tire in a worn state, wherein a modal frequency shift isreadily observable between the new and worn states.

FIG. 29 represents the results from a transmissibility simulation fornew and worn states of a given tire, illustrating transmissibility (indB) with respect to a spectrum of frequencies, again wherein a modalfrequency shift is readily observable between the new and worn states,and wherein the frequency shift may be applied to estimate a change inmass and accordingly a change in tire wear/tread depth.

In each of the aforementioned exemplary cases, the illustrated resultsare for the same tires, wherein the same shift in frequency is observedbetween worn and new tire models and implemented in the disclosed tirewear model.

Throughout the specification and claims, the following terms take atleast the meanings explicitly associated herein, unless the contextdictates otherwise. The meanings identified below do not necessarilylimit the terms, but merely provide illustrative examples for the terms.The meaning of “a,” “an,” and “the” may include plural references, andthe meaning of “in” may include “in” and “on.” The phrase “in oneembodiment,” as used herein does not necessarily refer to the sameembodiment, although it may.

The various illustrative logical blocks, modules, and algorithm stepsdescribed in connection with the embodiments disclosed herein can beimplemented as electronic hardware, computer software, or combinationsof both. To clearly illustrate this interchangeability of hardware andsoftware, various illustrative components, blocks, modules, and stepshave been described above generally in terms of their functionality.Whether such functionality is implemented as hardware or softwaredepends upon the particular application and design constraints imposedon the overall system. The described functionality can be implemented invarying ways for each particular application, but such implementationdecisions should not be interpreted as causing a departure from thescope of the disclosure.

The various illustrative logical blocks and modules described inconnection with the embodiments disclosed herein can be implemented orperformed by a machine, such as a general purpose processor, a digitalsignal processor (DSP), an application specific integrated circuit(ASIC), a field programmable gate array (FPGA) or other programmablelogic device, discrete gate or transistor logic, discrete hardwarecomponents, or any combination thereof designed to perform the functionsdescribed herein. A general purpose processor can be a microprocessor,but in the alternative, the processor can be a controller,microcontroller, or state machine, combinations of the same, or thelike. A processor can also be implemented as a combination of computingdevices, e.g., a combination of a DSP and a microprocessor, a pluralityof microprocessors, one or more microprocessors in conjunction with aDSP core, or any other such configuration.

The steps of a method, process, or algorithm described in connectionwith the embodiments disclosed herein can be embodied directly inhardware, in a software module executed by a processor, or in acombination of the two. A software module can reside in RAM memory,flash memory, ROM memory, EPROM memory, EEPROM memory, registers, harddisk, a removable disk, a CD-ROM, or any other form of computer-readablemedium known in the art. An exemplary computer-readable medium can becoupled to the processor such that the processor can read informationfrom, and write information to, the memory/storage medium. In thealternative, the medium can be integral to the processor. The processorand the medium can reside in an ASIC. The ASIC can reside in a userterminal. In the alternative, the processor and the medium can reside asdiscrete components in a user terminal.

Conditional language used herein, such as, among others, “can,” “might,”“may,” “e.g.,” and the like, unless specifically stated otherwise, orotherwise understood within the context as used, is generally intendedto convey that certain embodiments include, while other embodiments donot include, certain features, elements and/or states. Thus, suchconditional language is not generally intended to imply that features,elements and/or states are in any way required for one or moreembodiments or that one or more embodiments necessarily include logicfor deciding, with or without author input or prompting, whether thesefeatures, elements and/or states are included or are to be performed inany particular embodiment.

Whereas certain preferred embodiments of the present invention maytypically be described herein with respect to tire wear and/or tiretraction estimation for fleet management systems and more particularlyfor autonomous vehicle fleets or commercial trucking applications, theinvention is in no way expressly limited thereto and the term “vehicle”as used herein unless otherwise stated may refer to an automobile,truck, or any equivalent thereof, whether self-propelled or otherwise,as may include one or more tires and therefore require accurateestimation or prediction of tire wear and/or tire traction and potentialdisabling, replacement, or intervention in the form of for exampledirect vehicle control adjustments.

The term “user” as used herein unless otherwise stated may refer to adriver, passenger, mechanic, technician, fleet management personnel, orany other person or entity as may be, e.g., associated with a devicehaving a user interface for providing features and steps as disclosedherein.

The previous detailed description has been provided for the purposes ofillustration and description. Thus, although there have been describedparticular embodiments of a new and useful invention, it is not intendedthat such references be construed as limitations upon the scope of thisinvention except as set forth in the following claims.

What is claimed is:
 1. A method for estimating vehicle tire wear, themethod comprising: via one or more sensors associated with a vehicleand/or at least one tire of a plurality of tires supporting the vehicle,generating first data corresponding to real-time kinetics of the vehicleand/or the at least one tire; locally processing the first data togenerate second data as a reduced subset of the first data, said seconddata representative of the first data and comprising any one or morepredetermined features extracted therefrom; selectively transmitting thesecond data to a remote computing system via a communications network;via the remote computing system, processing the second data to estimatea wear characteristic for the at least one tire.
 2. The method of claim1, wherein the second data comprises a plurality of sequential dataframes, each data frame comprising a multidimensional histogram offorces associated with the vehicle and/or the at least one tire.
 3. Themethod of claim 2, further comprising selecting a subset of the dataframes between at least first and second events, and summarizing thedata frames over a particular time or a particular distance.
 4. Themethod of claim 3, wherein the summarizing of the data frames isperformed via local processing prior to transmittal of the summarizeddata frames to the remote computing system.
 5. The method of claim 3,wherein the subset of the data frames are transmitted to the remotecomputing system and the summarizing of the data frames is performed viathe remote computing system.
 6. The method of claim 3, furthercomprising correcting for missing data in a summarized data frame byscaling the summarized data frame by an expected number of data frameswith respect to an actual collected number of data frames.
 7. The methodof claim 1, wherein the step of processing the second data to estimatethe wear characteristic for the at least one tire comprises, via theremote computing system, processing the second data to generate thirddata corresponding to the first data, and further processing the thirddata to estimate the wear characteristic for the at least one tire. 8.The method of claim 7, wherein: the first data comprises CAN bussignals, the second data is generated via an encoding neural networklayer, the third data is generated via a decoding neural network layer,and a wear calculation layer is appended to the output of the decodingneural network layer and configured to transform the decoded CAN bussignals into instantaneous estimated wear values for the at least onetire.
 9. The method of claim 8, further comprising comparing theestimated wear values to actual wear values for the at least one tire togenerate an error value, and providing the error value as feedback tothe neural network layers.
 10. The method of claim 1, wherein theextracted features of the second data comprise wear performancecharacteristics representative of vehicle driving behavior.
 11. Themethod of claim 1, wherein processing the first data comprises a Fouriertransform on the first data and generating the second data comprisingextracted relevant frequencies and associated amplitudes.
 12. The methodof claim 1, wherein the second data comprises aggregated low frequencyCAN data corresponding to an amount of time spent by the vehicle in eachof one or more representative driving conditions.
 13. The method ofclaim 1, wherein the selective transmittal of second data isevent-based.
 14. The method of claim 1, wherein the selectivetransmittal of second data is time-based.
 15. A method for estimatingvehicle tire wear, the method comprising: via one or more sensorsassociated with a vehicle and/or at least one tire of a plurality oftires supporting the vehicle, generating first data corresponding toreal-time kinetics of the vehicle and/or the at least one tire; via aglobal positioning system transceiver, generating low frequency seconddata corresponding to vehicle positions; selectively transmitting thesecond data to a remote computing system via a communications network;via the remote computing system, processing the second data further inview of a vehicle model and one or more vehicle route characteristics togenerate third data corresponding to the first data, and furtherprocessing the third data to estimate the wear characteristic for the atleast one tire.
 16. The method of claim 15, wherein the second datafurther comprises a plurality of sequential data frames, each data framecomprising a multidimensional histogram of forces associated with thevehicle and/or the at least one tire, and wherein the remote computingsystem reconstructs a vehicle route from the collected vehicle positiondata and provides vehicle route feedback into the respectivemultidimensional histograms.
 17. A method for estimating vehicle tirewear, the method comprising: via one or more sensors associated with avehicle and/or at least one tire of a plurality of tires supporting thevehicle, generating first data corresponding to real-time kinetics ofthe vehicle and/or the at least one tire; processing the first data, viaa computing system onboard the vehicle, to generate second data as areduced subset of the first data, said second data representative of thefirst data and comprising any one or more predetermined featuresextracted therefrom; and via the onboard computing system, processingthe second data to estimate a wear characteristic for the at least onetire, and generating a notification associated with the estimated wearcharacteristic to a computing device associated with a vehicle user. 18.The method of claim 17, wherein the step of processing the second datato estimate the wear characteristic for the at least one tire comprisesprocessing the second data to generate third data corresponding to thefirst data, and further processing the third data to estimate the wearcharacteristic for the at least one tire.
 19. The method of claim 18,wherein: the first data comprises CAN bus signals, the second data isgenerated via an encoding neural network layer, the third data isgenerated via a decoding neural network layer, and a wear calculationlayer is appended to the output of the decoding neural network layer andconfigured to transform the decoded CAN bus signals into instantaneousestimated wear values for the at least one tire.
 20. The method of claim19, further comprising comparing the estimated wear values to actualwear values for the at least one tire to generate an error value, andproviding the error value as feedback to the neural network layers.